Computational workflows describe the complex multi-step methods that are used for data collection, data preparation, analytics, predictive modelling, and simulation that lead to new data products. They can inherently contribute to the FAIR data principles: by processing data according to established metadata; by creating metadata themselves during the processing of data; and by tracking and recording data provenance. These properties aid data quality assessment and contribute to secondary data usage. Moreover, workflows are digital objects in their own right. This paper argues that FAIR principles for workflows need to address their specific nature in terms of their composition of executable software steps, their provenance, and their development.
SEEK ID: https://workflowhub.eu/publications/7
DOI: 10.1162/dint_a_00033
Teams: FAIR Computational Workflows
Publication type: Journal
Journal: Data Intelligence
Citation: Data Intellegence 2(1-2):108-121
Date Published: 2020
Registered Mode: by DOI
Views: 2389
Created: 1st Dec 2021 at 21:43
Last updated: 16th Jan 2023 at 13:34
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